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A multimetric approach to analysis of genome-wide association by single markers and composite likelihood

A multimetric approach to analysis of genome-wide association by single markers and composite likelihood
A multimetric approach to analysis of genome-wide association by single markers and composite likelihood
Two case/control studies with different phenotypes, marker densities, and microarrays were examined for the most significant single markers in defined regions. They show a pronounced bias toward exaggerated significance that increases with the number of observed markers and would increase further with imputed markers. This bias is eliminated by Bonferroni adjustment, thereby allowing combination by principal component analysis with a Malecot model composite likelihood evaluated by a permutation procedure to allow for multiple dependent markers. This intermediate value identifies the only demonstrated causal locus as most significant even in the preliminary analysis and clearly recognizes the strongest candidate in the other sample. Because the three metrics (most significant single marker, composite likelihood, and their principal component) are correlated, choice of the n smallest P values by each test gives <3n regions for follow-up in the next stage. In this way, methods with different response to marker selection and density are given approximately equal weight and economically compared, without expressing an untested prejudice or sacrificing the most significant results for any of them. Large numbers of cases, controls, and markers are by themselves insufficient to control type 1 and 2 errors, and so efficient use of multiple metrics with Bonferroni adjustment promises to be valuable in identifying causal variants and optimal design simultaneously.
design, analysis, genome, principal component analysis, phenotype, research support, weight, human, biological markers, combination, methods, research, genetics
0027-8424
2592-2597
Gibson, Jane
855033a6-38f3-4853-8f60-d7d4561226ae
Tapper, William
9d5ddc92-a8dd-4c78-ac67-c5867b62724c
Cox, David
fadea63c-c8f3-452f-a6b5-3584f5e3be43
Zhang, Weihua
1a759991-f2d4-4324-b8e2-c5b4c2b527d6
Pfeufer, Arne
865e4426-6791-4d22-ae9f-e3acab4bf33c
Gieger, Christian
45f48b74-8ab8-4be6-ad54-91e61163ed5e
Wichmann, H.-Erich
62ffdcec-10a3-47c5-91e6-6106c4dbd6cd
Kaab, Stefan
2a3d2520-b836-4197-9239-d96a074ac8cd
Collins, Andrew R.
7daa83eb-0b21-43b2-af1a-e38fb36e2a64
Meitinger, Thomas
cee5677e-e82d-4212-a028-acc602ad0378
Morton, Newton
c668e2be-074a-4a0a-a2ca-e8f51830ebb7
Gibson, Jane
855033a6-38f3-4853-8f60-d7d4561226ae
Tapper, William
9d5ddc92-a8dd-4c78-ac67-c5867b62724c
Cox, David
fadea63c-c8f3-452f-a6b5-3584f5e3be43
Zhang, Weihua
1a759991-f2d4-4324-b8e2-c5b4c2b527d6
Pfeufer, Arne
865e4426-6791-4d22-ae9f-e3acab4bf33c
Gieger, Christian
45f48b74-8ab8-4be6-ad54-91e61163ed5e
Wichmann, H.-Erich
62ffdcec-10a3-47c5-91e6-6106c4dbd6cd
Kaab, Stefan
2a3d2520-b836-4197-9239-d96a074ac8cd
Collins, Andrew R.
7daa83eb-0b21-43b2-af1a-e38fb36e2a64
Meitinger, Thomas
cee5677e-e82d-4212-a028-acc602ad0378
Morton, Newton
c668e2be-074a-4a0a-a2ca-e8f51830ebb7

Gibson, Jane, Tapper, William, Cox, David, Zhang, Weihua, Pfeufer, Arne, Gieger, Christian, Wichmann, H.-Erich, Kaab, Stefan, Collins, Andrew R., Meitinger, Thomas and Morton, Newton (2008) A multimetric approach to analysis of genome-wide association by single markers and composite likelihood. Proceedings of the National Academy of Sciences, 105 (7), 2592-2597. (doi:10.1073/pnas.0711903105).

Record type: Article

Abstract

Two case/control studies with different phenotypes, marker densities, and microarrays were examined for the most significant single markers in defined regions. They show a pronounced bias toward exaggerated significance that increases with the number of observed markers and would increase further with imputed markers. This bias is eliminated by Bonferroni adjustment, thereby allowing combination by principal component analysis with a Malecot model composite likelihood evaluated by a permutation procedure to allow for multiple dependent markers. This intermediate value identifies the only demonstrated causal locus as most significant even in the preliminary analysis and clearly recognizes the strongest candidate in the other sample. Because the three metrics (most significant single marker, composite likelihood, and their principal component) are correlated, choice of the n smallest P values by each test gives <3n regions for follow-up in the next stage. In this way, methods with different response to marker selection and density are given approximately equal weight and economically compared, without expressing an untested prejudice or sacrificing the most significant results for any of them. Large numbers of cases, controls, and markers are by themselves insufficient to control type 1 and 2 errors, and so efficient use of multiple metrics with Bonferroni adjustment promises to be valuable in identifying causal variants and optimal design simultaneously.

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More information

Published date: 19 February 2008
Keywords: design, analysis, genome, principal component analysis, phenotype, research support, weight, human, biological markers, combination, methods, research, genetics
Organisations: Human Genetics, Medicine, Health Sciences

Identifiers

Local EPrints ID: 59748
URI: https://eprints.soton.ac.uk/id/eprint/59748
ISSN: 0027-8424
PURE UUID: 6285f722-f50e-4b48-bd33-ee49a7618ba4
ORCID for Jane Gibson: ORCID iD orcid.org/0000-0002-0973-8285
ORCID for Andrew R. Collins: ORCID iD orcid.org/0000-0001-7108-0771

Catalogue record

Date deposited: 04 Sep 2008
Last modified: 20 Jul 2019 01:23

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Contributors

Author: Jane Gibson ORCID iD
Author: William Tapper
Author: David Cox
Author: Weihua Zhang
Author: Arne Pfeufer
Author: Christian Gieger
Author: H.-Erich Wichmann
Author: Stefan Kaab
Author: Thomas Meitinger
Author: Newton Morton

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